Earlier this year, we overhauled our permissions system at GoCardless. It’s given us a platform to build exciting tooling, as well as increasing our confidence and visibility of the system. This post shares some of the thought processes and lessons learned.

The aim of the project was to pull together all the code we had that answered the question ‘Can I Do This Thing’. For us, the main focus was our API - we have a vast number of endpoints with subtle rules applied governing who can do what. We had three objectives:

  1. Visibility: anyone can easily see who is able to use a particular endpoint.
  2. Confidence: we can be confident in the rules we are applying, and can change those rules.
  3. Enablement: build a platform to support future tooling.


Any permissions system is built out of rules. A rule is a piece of logic such as ‘users with read-write access can do [x]’ or ‘accounts on the basic package cannot do [y]’. In my experience, these rules naturally grow more complex over time, and a periodic review can be very useful.

Permission vs. Restrictions

You can frame a ‘who can do what’ rule in two ways: either via permissions (rules that permit access) or restrictions (rules that prevent access). Each model naturally enables different structures. Permissions works well when most of your rules are applied using a UNION - i.e. if you fulfill criteria (a) or criteria (b) then you can do the thing. Restrictions works better when you are using INTERSECTION - i.e. id you fulfill criteria (a) and criteria (b) then you can do the thing.

You may need to use both in your system, but try to avoid it if you can as it makes your rules harder to define and follow. I’d recommend using a hierarchy if you need - so either having a top level permissions model where some of your rules include INTERSECTIONS inside, or a top level restrictions model where some of your rules include UNION inside.

Scoping the project

As part of scoping, we first had to catalogue all the rules we were applying in our API (which was a non-trivial exercise, unfortunately). We chose to pursue a restrictions model, largely because that’s how the existing system was structured, but also because it better suited our use case where the majority of our rules were combined using INTERSECTIONS. To be honest, I suspect this would be true for most public APIs.

Our next major challenge was to decide how we wanted to declare and manage the rules. We would ideally have liked to encode all our restrictions logic into a token, which ‘knows’ what it can and cannot do. For various reasons that wasn’t possible for us right now, so all our thinking revolves around the idea that (for now) our rules are applied by the endpoints themselves, at the point that the API request is made.

Rule Catalogue

We quickly agreed that we wanted to have a catalogue of rules which could be applied to different endpoints, and were well documented. The same code would run on all of our API requests, and apply the rules so we could be confident that they were consistent and correct. It also made our testing approach simpler as there was a single component that could be battle tested rather than the logic spread across our code base. Additionally, because all the rules were applied using an INTERSECTION, we could unit test each rule individually and then test a few combinations, as the combination logic was trivial (apply all the rules, if you pass them all then continue). Any complicated logic was contained within a particular rule, and then unit tested carefully.

Which rules does each endpoint apply?

The next step was to understand how rules from this catalogue were applied to particular endpoints. We came up with two contrasting approaches, both of which were in use in the API when we started.

Option 1: Centrally Defined Restrictions

In this design you have a single central location which details which endpoints are included in a particular rule, as well as a centrally defined logic for that rule. For example, you might have a single file which has a list of read-write endpoints which read-only users cannot access.

The key advantage of this model is that it’s easy to answer the question ‘what can a user with attribute [x] do’. For example, what endpoints are unlocked by upgrading an account from basic to pro. This visibility can be useful internally too - what extra permissions does a support team member get if I give them ‘tier 2’ access?

There are options of how you store these restrictions: you could store them in a database rather than in code, although we abandoned that idea due to the challenges of maintaining the rules across multiple environments.

Option 2: Endpoint Owned Restrictions

Instead of centrally defining the restrictions, you can instead define them alongside the endpoint definition. Each endpoint then ‘owns’ its own restrictions, and they can be easily reviewed next to the endpoint’s code.

The main advantage here is that it’s easy to answer the question ‘who can access endpoint [x]’. For example, what restrictions do we apply to someone wanting to create a new customer. It makes the rules very discoverable (as they’re in the endpoint definition itself) and makes the ‘I forgot to restrict my route’ bug less likely as they restrictions are clearly part of the code review.

What we did

We wanted to have our cake and eat it: there were proponents of both options with strong arguments, and for once, we found a way.

We chose option 2 but added a script (enforced by our CI pipeline) that generates two JSON files from the endpoint definitions and pulls all the applied rules into a single location. One file is restrictions_by_route which answers the ‘who can do [x]’ question. The second file is routes_by_restriction which answers the ‘what can someone with [x] flag do’ question.


This work enabled us to expose an endpoint that answered the question ‘what endpoints can I access?’.

N.b.: this endpoint already existed, but it only included about 30% of our rules

This is a really powerful feature to power your UI - it allowed us to strip out duplicated logic in our front end which was being used to conditionally render certain buttons, and rely completely on the API as our single source of truth for who can do what. There are a few other ways of achieving this (we discussed using OPTIONS calls everywhere instead) but this was a much simpler solution for us to implement.

Double Bonus

The best bit about the new endpoint was that we could add a feature we’d wanted for ages: ‘what endpoints could I use if I wasn’t a GoCardless staff user’. Our API has a number of features that are restricted to just GoCardless staff. Some of these features are exposed in the dashboard, and to a new joiner it’s impossible to tell what they can do ‘because they’re a GC user’ and what an external user would see. This makes onboarding really tough as our support teams have to learn all the different rules about what features are available to external and internal users. We now use this endpoint to conditionally render a bright pink box around any content which is only visible ‘because you are a staff user’, which has really helped our internal users.

My favourite part about this is that the code to achieve it in the UI is really simple:

const UnlessUserIsRestricted = ({
  isUserRestricted, // injected from redux
  wouldUserBeRestrictedIfNotGCAdmin, // injected from redux
}) => {
  if (isUserRestricted(endpoint)) {

  if (wouldUserBeRestrictedIfNotGCAdmin(endpoint)) {
    return <div className="u-admin-only">{children}</div>; // this adds the pink box

  return children;
return (
  <UnlessUserIsRestricted resource="customers" action="create">
    <Button>Create Customer</Button>

So How Did We Do?

  1. Visibility: our JSONs provide a single source of truth for ‘who can do what’.
  2. Confidence: we have 700 lines of tests unit testing each of our rules, and we can easily change which rules are applied to each endpoint.
  3. Enablement: we’ve enabled the bonus projects to reduce complexity and duplication in our UI, and help out our internal users along the way.

This might be the thing I’m most proud of that I’ve done at GC so far. I hope you enjoyed the read!